Spatio-temporal distribution of malaria in Betong, Sarawak, Malaysia: a five years study
The emergence of malaria has become one of the major public health problems in Betong, Sarawak, Malaysia. The number of reported malaria cases are increasing continuously in recent years. The aim of this study was to analyse the spatio-temporal pattern based on the yearly malaria surveillance data....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Pusat Sistematik Serangga, Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/14651/1/31991-116253-1-PB.pdf http://journalarticle.ukm.my/14651/ http://ejournals.ukm.my/serangga/issue/view/1237/showToc |
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| Institution: | Universiti Kebangsaan Malaysia |
| Language: | English |
| Summary: | The emergence of malaria has become one of the major public health problems in Betong, Sarawak, Malaysia. The number of reported malaria cases are increasing continuously in recent years. The aim of this study was to analyse the spatio-temporal pattern based on the yearly malaria surveillance data. Descriptive analysis was done to investigate the malaria incidence by time, person and place. Further analysis was done by mapping all malaria cases reported from year 2013 to 2017 by using ArcGIS software. Distribution of malaria cases were mapped in term of crude incidence. The average nearest neighbour was used to determine the distance analysis between malaria cases while Kernel density was applied to detect spatial pattern of locality for malaria hotspots. Distribution of malaria cases was clustered and random based on distance analysis. Based on spatio-temporal analysis pattern, malaria cases were identified as clusters in Betong and Spaoh subdistricts. It was observed that high risk occurrence of malaria cases were reported in the months of July to October each year. All the socio-demographic variables were associated with the malaria infection. After adjusting the relationship of all potential predictors at P<0.05, potential predictors such as gender, ethnicity (excluding the Malays) and occupation had significant association with the malaria infection. Spatial mapping could be beneficial to visualize the distribution of malaria cases for public health prevention. |
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